spdep (version 0.4-9)

probmap: Probability mapping for rates

Description

The function returns a data frame of rates for counts in populations at risk with crude rates, expected counts of cases, relative risks, and Poisson probabilities.

Usage

probmap(n, x, row.names=NULL)

Arguments

n

a numeric vector of counts of cases

x

a numeric vector of populations at risk

row.names

row names passed through to output data frame

Value

• raw
  raw (crude) rates
• expCount
  expected counts of cases assuming global rate
• relRisk
  relative risks: ratio of observed and expected counts of cases multiplied by 100
• pmap
  Poisson probability map values: probablility of getting a more ``extreme'' count than actually observed - here two-tailed, with extreme tails indicating ``unusual'' values

Details

The function returns a data frame, from which rates may be mapped after class intervals have been chosen. The class intervals used in the examples are mostly taken from the referenced source.

References

Bailey T, Gatrell A (1995) Interactive Spatial Data Analysis, Harlow: Longman, pp. 300--303.

See Also

EBest, EBlocal, ppois

Examples

example(auckland)
res <- probmap(auckland$M77_85, 9*auckland$Und5_81)
brks <- c(-Inf,2,2.5,3,3.5,Inf)
cols <- grey(6:2/7)
plot(auckland, col=cols[findInterval(res$raw*1000, brks, all.inside=TRUE)])
legend("bottomleft", fill=cols, legend=leglabs(brks), bty="n")
title(main="Crude (raw) estimates of infant mortality per 1000 per year")
brks <- c(-Inf,47,83,118,154,190,Inf)
cols <- cm.colors(6)
plot(auckland, col=cols[findInterval(res$relRisk, brks, all.inside=TRUE)])
legend("bottomleft", fill=cols, legend=leglabs(brks), bty="n")
title(main="Standardised mortality ratios for Auckland child deaths")
brks <- c(0,0.05,0.1,0.2,0.8,0.9,0.95,1)
cols <- cm.colors(7)
plot(auckland, col=cols[findInterval(res$pmap, brks, all.inside=TRUE)])
legend("bottomleft", fill=cols, legend=leglabs(brks), bty="n")
title(main="Poisson probabilities for Auckland child mortality")